| Title |
Acknowledging crossing-avoidance heuristic violations when solving the Euclidean travelling salesperson problem
|
|---|---|
| Published in |
Psychological Research, June 2017
|
| DOI | 10.1007/s00426-017-0881-7 |
| Pubmed ID | |
| Authors |
Markos Kyritsis, Stephen R. Gulliver, Eva Feredoes |
| Abstract |
If a salesperson aims to visit a number of cities only once before returning home, which route should they take to minimise the total distance/cost? This combinatorial optimization problem is called the travelling salesperson problem (TSP) and has a rapid growth in the number of possible solutions as the number of cities increases. Despite its complexity, when cities and routes are represented in 2D Euclidean space (ETSP), humans solve the problem with relative ease, by applying simple visual heuristics. One of the most important heuristics appears to be the avoidance of path crossings, which will always result in more optimal solutions than tours that contain crossings. This study systematically investigates whether the occurrence of crossings is impacted by geometric properties by modelling their relationship using binomial logistic regression as well as random forests. Results show that properties, such as the number of nodes making up the convex hull, the standard deviation of the angles between nodes, the average distance between all nodes in the graph, the total number of nodes in the graph, and the tour cost (i.e., a measure of performance), are significant predictors of whether crossings are likely to occur. |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 22% |
| Student > Ph. D. Student | 2 | 11% |
| Other | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Lecturer | 1 | 6% |
| Other | 2 | 11% |
| Unknown | 7 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Business, Management and Accounting | 3 | 17% |
| Computer Science | 2 | 11% |
| Engineering | 2 | 11% |
| Economics, Econometrics and Finance | 1 | 6% |
| Neuroscience | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 8 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2017.
All research outputs
#7,532,940
of 22,985,065 outputs
Outputs from Psychological Research
#288
of 973 outputs
Outputs of similar age
#120,744
of 317,422 outputs
Outputs of similar age from Psychological Research
#5
of 23 outputs
Altmetric has tracked 22,985,065 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 973 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,422 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.